The widespread adoption of speech-based online services raises security and
privacy concerns regarding the data that they use and share. If the data were
compromised, attackers could exploit user speech to bypass speaker verification
systems or even impersonate users. To mitigate this, we propose DeID-VC, a
speaker de-identification system that converts a real speaker to pseudo
speakers, thus removing or obfuscating the speaker-dependent attributes from a
spoken voice. The key components of DeID-VC include a Variational Autoencoder
(VAE) based Pseudo Speaker Generator (PSG) and a voice conversion Autoencoder
(AE) under zero-shot settings. With the help of PSG, DeID-VC can assign unique
pseudo speakers at speaker level or even at utterance level. Also, two novel
learning objectives are added to bridge the gap between training and inference
of zero-shot voice conversion. We present our experimental results with word
error rate (WER) and equal error rate (EER), along with three subjective
metrics to evaluate the generated output of DeID-VC. The result shows that our
method substantially improved intelligibility (WER 10% lower) and
de-identification effectiveness (EER 5% higher) compared to our baseline. Code
and listening demo: this https URL

提出了一种名为 DeID-VC 的系统，利用变分自编码器（VAE）生成伪人声以保护用户的语音隐私，可在发言者或话语层面分配独特的伪人声，并通过两个新的学习目标缩小了训练和推理之间的差距，实验结果表明与基线相比，DeID-VC 方法在可读性和去识别性方面都有显著改善。

Zero-shot 伪造语音进行说话人去识别

DeID-VC: Speaker De-identification via Zero-shot Pseudo Voice Conversion

Due to a constantly increasing amount of speech data that is stored in
different types of databases, voice privacy has become a major concern. To
respond to such concern, speech researchers have developed various methods for
speaker de-identification. The state-of-the-art solutions utilize deep learning
solutions which can be effective but might be unavailable or impractical to
apply for, for example, under-resourced languages. Formant modification is a
simpler, yet effective method for speaker de-identification which requires no
training data. Still, remaining intonational patterns in formant-anonymized
speech may contain speaker-dependent cues. This study introduces a novel
speaker de-identification method, which, in addition to simple formant shifts,
manipulates f0 trajectories based on functional data analysis. The proposed
speaker de-identification method will conceal plausibly identifying pitch
characteristics in a phonetically controllable manner and improve formant-based
speaker de-identification up to 25%.

本研究提出了一种新颖的说话者去识别方法，采用简单的共振峰偏移和基于函数数据分析的 f0 轨迹操作，可以在音素可控的方式下掩盖潜在的识别音调特征，提高了基于共振峰的语音匿名度最多达 25%。